Deep Learning for Scene Classification: A Survey
It serves as a resource for researchers in computer vision to understand advances in scene classification, but it is incremental as it summarizes existing work rather than introducing new methods.
This paper provides a comprehensive survey of recent achievements in scene classification using deep learning, covering over 200 publications and including challenges, datasets, taxonomy, and performance comparisons.
Scene classification, aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challenging problem in computer vision. The rise of large-scale datasets, which constitute the corresponding dense sampling of diverse real-world scenes, and the renaissance of deep learning techniques, which learn powerful feature representations directly from big raw data, have been bringing remarkable progress in the field of scene representation and classification. To help researchers master needed advances in this field, the goal of this paper is to provide a comprehensive survey of recent achievements in scene classification using deep learning. More than 200 major publications are included in this survey covering different aspects of scene classification, including challenges, benchmark datasets, taxonomy, and quantitative performance comparisons of the reviewed methods. In retrospect of what has been achieved so far, this paper is also concluded with a list of promising research opportunities.